This session continues our education series of the mathematics underpinning machine learning algorithms. In this session we will do a quick refresher on basic probabilities and will cover Binary Logistic Regression. We will talk about the rules of probability and the Bernoulli distribution. We will derive the Logistic Regression model and we will look at techniques for estimating the model parameters. We will discuss certain principal assumptions of Logistic Regression, and we will see the model in action, and how it is typically used with a classification dataset.
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Probabilities and Logistic Regression
Nikolay has over 10 years of database experience and has been involved in large scale migration, consolidation, and data warehouse deployment projects in the UK and abroad. He is a speaker, blogger, author of numerous articles and a book on advanced database topics. For the last three years Nikolay has been working exclusively in the big data (Hadoop) space with focus on Spark and machine learning. He has an M.Sc. in Software Technologies and is working towards an M.Sc. in Data Science.